Transcript of "F0361026033"

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International Journal of Computational Engineering Research||Vol, 03||Issue, 6||www.ijceronline.com ||June ||2013|| Page 26Report on Reputation Based Data Aggregation ForWireless NetworkBhoopendra Singh M Tech (CSE) 1, Mr. Gaurav Dubey21Amity University ,Noida, India2Amity School of Engineering and Technology, Noida, IndiaI. INTRODUCTIONThe field of wireless sensor networks combines sensing, computation, and communication into a singletiny device. Through advanced mesh networking protocols, these devices form a sea of connectivity that extendsthe reach of cyber space out into the physical world. As water flows to fill every room of a submerged ship, themesh networking connectivity will seek out and exploit any possible communication path by hopping data fromnode to node in search of its destination. The power of wireless sensor networks lies in the ability to deploylarge numbers of tiny nodes that assemble and configure themselves. Usage scenarios for these devices rangefrom real time tracking, to monitoring of environmental conditions, to ubiquitous computer environment, to insitu monitoring of the health of structures or equipment. The application demands for robust, scalable, low-costand easy to deploy networks are perfectly met by a wireless sensor network. If one of the nodes should fail, anew topology would be selected and the overall network would continue to deliver data. If more nodes areplaced in the field, they only create more potential routing opportunities. There is extensive research in thedevelopment of new algorithms for data aggregation, ad-hoc routing and distributed signal processing in contextof wireless sensor networks. As the algorithms and protocols for wireless sensor network are developed, theymust be supported by a low power, efficient and flexible hardware platform.1.1 Overview of wireless sensor networkThe concept of wireless sensor networks is based on a simple equation:Sensing+CPU+Tranceiver=Thousands of potential applicationsAs soon as people understand the capabilities of a wireless sensor network, hundreds of applicationsspring to mind. It seems like a straightforward combination of modern technology. However , actuallycombining sensors , radios and CPUs into an effective wirelss sensor network requires a detailed understandingof the both capabilities and limitations of each of the underlying hardware components, as well as detailedunderstanding of modern networking technologies and distributed systems theory. Each individual node must bedesigned to provide the set of primitives necessary to synthesize the interconnected web that will emerge as theyare deployed, while meeting strict requirements of size, cost and power consumption. Recent advances in micro-electro-mechanical systems (MEMS) technology, wireless communications, and digital electronics have enabledthe development of low-cost, low-power, multifunctional sensor nodes that are small in size and communicate inABSTRACT:wireless sensor network consists of spatially distributed autonomous sensors to monitorphysical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperativelypass their data through the network to a main location.In wireless networks, malicious sensor nodessend false data reports to distort aggregation results. Existing trust systems rely on general reputation tomitigate the effect of this attack. This report is the implementation of one of a novel reliable dataaggregation protocol, called RDAT i.e Reliable Data Aggregation Protocol.In this report Reliable DataAggregation Protocol with functional reputation is implemented. It is based on the concept of functionalreputation. Functional reputation enables data aggregators to evaluate each type of sensor node actionusing a respective reputation value thereby increasing the accuracy of the trust system. The simulationresults show that protocol RDAT significantly improves the reliability of aggregated data in the presenceof compromised nodes.

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Report On Reputation….www.ijceronline.com ||June ||2013|| Page 27short distances. These tiny sensor nodes, which consist of sensing, data processing, and communicatingcomponents, leverage the idea of sensor networks based on collaborative effort of a large number of nodes.Sensor networks represent a significant improvement over traditional sensors, which are deployed in thefollowing two ways :• Sensors can be positioned far from the actual phenomenon, i.e., something known by sense perception. In thisapproach, large sensors that use some complex techniques to distinguish the targets from environmental noiseare required.• Several sensors that perform only sensing can be deployed. The positions of the sensors and communicationstopology are carefully engineered.They transmit time series of the sensed phenomenon to the central nodes where computations areperformed and data are fused. A sensor network is composed of a large number of sensor nodes, which aredensely deployed either inside the phenomenon or very close to it. The position of sensor nodes need not beengineered or pre-determined. This allows random deployment in inaccessible terrains or disaster reliefoperations. On the other hand, this also means that sensor network protocols and algorithms must possess self-organizing capabilities. Another unique feature of sensor networks is the cooperative effort of sensor nodes.Sensor nodes are fitted with an on-board processor. Instead of sending the raw data to the nodes responsible forthe fusion, sensor nodes use their processing abilities to locally carry out simple computations and transmit onlythe required and partially processed data.1.2 Classification System DesignClassification plays a vital role in many information management and retrieval tasks. Based on theorganization of categories, The sensor nodes are usually scattered in a sensor field as shown in Fig. 1. Each ofthese scattered sensor nodes has the capabilities to collect data and route data back to the sink and the end users.Data are routed back to the end user by a multi-hop infrastructure less architecture through the sink.The sinkmay communicate with the task manager node via Internet or Satellite. This protocol stack combines power androuting awareness, integrates data with networking protocols, communicates power efficiently through thewireless medium, and promotes cooperative efforts of sensor nodes. The protocol stack consists of theapplication layer, transport layer, network layer, data link layer, physical layer, power management plane,mobility management plane, and task management plane. Depending on the sensing tasks, different types ofapplication software can be built and used on the application layer. The transport layer helps to maintain theflow of data if the sensor networks application requires it. The network layer takes care of routing the datasupplied by the transport layer. Since the environment is noisy and sensor nodes can be mobile, the MACprotocol must be power aware and able to minimize collision with neighbors’ broadcast. The physical layeraddresses the needs of a simple but robust modulation, transmission and receiving techniques. In addition, thepower, mobility, and task management planes monitor the power, movement, and task distribution among thesensor nodes. These planes help the sensor nodes coordinate the sensing task and lower the overall powerconsumption.The power management plane manages how a sensor node uses its power. For example, the sensor node mayturn off its receiver after receiving a message from one of its neighbors. This is to avoid getting duplicatedmessages. Also, when the power level of the sensor node is low, the sensor node broadcasts to its neighbors thatit is low in power and cannot participate in routing messages. The remaining power is reserved for sensing. Themobility management plane detects and registers the movement of sensor nodes, so a route back to the user isalways maintained, and the sensor nodes can keep track of who are their neighbor sensor nodes. By knowingwho the neighbor sensor nodes are, the sensor nodes can balance their power and task usage. The taskmanagement plane balances and schedules the sensing tasks given to a specific region. Not all sensor nodes inthat region are required to perform the sensing task at the same time. As a result, some sensor nodes perform thetask more than the others depending on their power level. These management planes are needed, so that sensornodes can work together in a power efficient way, route data in a mobile sensor network, and share resourcesbetween sensor nodes. Without them, each sensor node will just work individually. From the whole sensor

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Report On Reputation….www.ijceronline.com ||June ||2013|| Page 28network standpoint, it is more efficient if sensor nodes can collaborate with each other, so the lifetime of thesensor networks can be prolonged.II. APPROACHES2.1 Functional Reputation Based Data AggregationConsider a large sensor network with densely deployed sensor nodes. Due to the dense deployment,sensor nodes have overlapping sensing ranges and events are detected by multiple sensor nodes. Hence,aggregation of correlated data at neighboring sensor nodes is needed. Some sensor nodes are dynamicallydesignated as data aggregators to aggregate data from their neighboring sensor nodes, although every sensornode is assumed to be capable of doing data aggregation. To balance the energy consumption of sensor nodes,the role of data aggregator is rotated among sensor nodes based on their residual energy levels. Sensor nodeshave limited computation and communication capabilities. For example, the Mica2 motes have a 4Mhz 8bitAtmel microprocessor, and are equipped with an instruction-memory of 128KB and a RAM of 4KB. Allmessages are time-stamped and nonces are used to prevent reply attacks. Sensor nodes employ monitoringmechanisms to detect malicious activities of their neighbours. Sensor nodes establish pairwise shared keys withtheir neighbours using an existing random key distribution protocols . Pairwise keys are used for dataauthentication. Data are transmitted in plain text unless it is stated otherwise. Intruders can compromise sensornodes via physical capturing or through the radio communication channel. Once a sensor node is compromised,all information of the node becomes available to the intruder. Although compromised nodes can perform manytypes of attacks to degrade the network’s security and performance, we only consider the attacks againstintegrity of the aggregated data. We assume that compromised nodes send false data (sensing reports) to dataaggregators. If a compromised node is selected as data aggregator it can inject false data into aggregated data. Inaddition, compromised nodes selectively forward and misdirect aggregated data to distort the integrity of theaggregated data.2.2 Reliable data aggregation protocol (RDAT)The basic idea behind protocol RDAT is to evaluate trustworthiness of sensor nodes by usingthree types of functional reputation, namely sensing, routing, and aggregation .Sensor nodes monitortheir neighborhood to obtain first-hand information regarding their neighboring nodes. For sensing,routing, and aggregation tasks, each sensor node Ni records good and bad actions of its neighbors in atable referred to as functional reputation table. Functional reputation tables are exchanged amongsensor nodes to be used as second-hand information during trust evaluation. The functional reputationtables are piggy backed to other data and control packets in order to reduce the data transmissionoverhead. When sensor node Ni needs to interact with its neighbour Nk , Ni evaluates thetrustworthiness of Nk using both first-hand and second-hand information regarding Nk . Functionalreputation for aggregation (Ra,baggregation) is needed by sensor nodes to evaluate thetrustworthiness of data aggregators. Functional reputations for routing (Ra,brouting)and sensing(Ra,bsensing) are used by data aggregators to increase the security and reliability of the aggregateddata. Functional reputation values are quantified using beta distributions of node actions as explainednext.2.3 Beta reputation systemAs the success of Bayesian formulation in detecting arbitrary misbehavior of sensor nodes is , we selecta Bayesian formulation, namely beta reputation system, for trust evolution. In this section, before giving thedetails of protocol RDAT, we present a brief information about beta reputation system. Posteriori probabilitiesof binary events can be represented as beta distributions which is indexed by the two parameters α and β . Thebeta distribution f (p|α,β) can be expressed using the gamma function Γ as:f (p|α,β) = (Γ(α+β)/ Γ(α)+Γ(β)) pα−1(1− p)β−10 ≤ p ≤ 1, α > 0, β > 0The probability expectation value of the beta distribution is given by E(p) = α/(α+β). To show that how betafunction can be employed in sensor networks let us consider the task of target detection as an action with two

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Report On Reputation….www.ijceronline.com ||June ||2013|| Page 29possible outcomes, namely “correct” and “false”. Let r be the observed number of “correct” target detectionsand s be the the observed number of “false” target detections by a sensor node. The beta function takes theinteger number of past observations of “correct” and “false” target detections to predict the expected frequencyof “correct” target detections by that sensor node in the future which is achieved by setting:α = r+1 β = s+1, where r, s ≥ 0.The variable p represents the probability of “correct” target detections and f (p|α,β) represents the probabilitythat p has a specific value. The probability expectation value is given by E(p) which is interpreted as the mostlikely value of p. Hence, a sensor node’s reliability can be predicted by beta distribution function of its previousactions as long as the actions are represented in binary format.2.4 Computing functional reputation and trustFunctional reputation value (Ra,bX) is computed using beta density function of sensor node Nk’sprevious actions with respect to function X. Trust (Ti,jX) is the expected value of Ra,bX.Let us take routing taskas an example. If sensor node Ni counts the number of good and bad routing actions of Nk as α and β,respectively. Then, Ni computes the functional reputation Ra,broutingabout node Nk as Beta(α+1,β+1).Following the definition of trust, Ti,jroutingis calculated as the expected value of Ra,broutingTi,jrouting= E(Beta(α+1,β+1))= α+1/α+β+2This equation shows that the expected value of the beta distribution is simply the fraction of events that havehad outcome α. Hence, functional reputation value of routing is given by the ratio of good routing actions tototal routing actions observed. This is an intuitive decision and it justifies the use of the beta distribution. In theabove formula, Ra,broutingrepresents node Ni’s observations about node Nk . In other words, it just involvesfirst-hand information. Reputation systems that depend on only first-hand information has a very largeconvergence time . Hence, second-hand information is desirable in order to confirm firsthand information. Inprotocol RDAT, neighboring sensor nodes exchange their functional reputation tables to provide secondhandinformation and this information is included in trust evaluation. Let us assume that sensor node Ni receivessecondhand information about node Nk from a set of N nodes and Sinfo(rk,j ) represents the second-handinformation received from node Nk (k ∈ N). Ni already has previous observations about Nj as αi,k and βi,j.Further assume that, in a period of Δt, Ni records ra,b good routing actions and si,j bad routing actions of Nk .Then, Ni computes the trust Ti,jroutingfor Nk as follows.αi,jrouting= ν*αi,j + ra,b+ Σ Sinforouting(rk,j )βi,jrouting= ν*βi,j + ri,j+ Σ Sinforouting(rk,j )Ti,jrouting= E(beta(αi,jrouting+1, βi,jrouting+1))where ν < 1 is the aging factor that allows reputation to fade with time. Integration of first and second handinformation into a single reputation value is studied in by mapping it to Dempster-Shafer belief theory . Wefollow a similar approach and use the reporting node Nk’s reputation to weight down its contribution to thereputation of node Nk . Hence, second-hand information Sinfo(rk,j )is defined asSinfo(rk,j )= (2*αi,k * rk,j)/((βi,k +2) * (rk,j + sk,j +2) * (2 * αi,k ))Sinfo(sk,j )= (2*αi,k * sk,j)/((βi,k +2) * (rk,j + sk,j +2) * (2 * αi,k ))

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Report On Reputation….www.ijceronline.com ||June ||2013|| Page 30The idea here is to give greater weight to nodes with high trust and never give a weight above 1 so that second-hand information does not outweigh first-hand information. In this function, if αi,k = 0 the function returns 0,therefore node Nk’s report does not affect the reputation update.2.5 Secure and reliable data aggregationIn protocol RDAT, data aggregation is periodically performed in certain time intervals. Ineach data aggregation session, secure and reliable data aggregation is achieved in two phases. In thefirst phase, before transmitting data to data aggregators, each sensor node Ni computesRa,baggregationvalue for its data aggregator Aj and evaluate the trustworthiness of Aj . Iftrustworthiness of Aj is below a predetermined threshold, then Ni does not let Aj to aggregate its data.To achieve this, Ni encrypts its data using the pairwise key that is shared between the base station andNi and sends this encrypted data to the base station along with a report indicating Ajmay becompromised. Based on the number of reports about Aj over the time, the base station may decide thatAj is a compromised node and it should be revoked from the network. In the second phase of dataaggregation session, the following Reliable Data Aggregation (RDA) algorithm is run by dataaggregators. Algorithm RDA depends on Ra,bsensingand Ra,broutingfunctional reputation values tomitigate the effect of compromised sensor nodes on aggregated data.The Algorithm RDA is- Input: Data aggregator Aj , Aj’s neighboring nodes {N1,N2, ...,Ni}, trust values ofneighboring nodes computed by Aj { Tj,1sensing,..., Tj,isensing} and{ Tj,1routing,...,Tj,irouting }. Output: Aggregated data Dagg . Step 1: Aj requests each Ni to send its data for data aggregation. Step 2: Sensor nodes {N1,N2, ...,Ni} transmit data {D1,D2, ...,Di} to Aj . Step 3: Aj updates trust values Ti,jsensingand Ti,jroutingof each Ni based on the first andsecond hand information regarding Ni . Step 4: Aj weights data Di of sensor node Ni using the Ti,jsensingand Ti,jrouting. Step 5: Aj aggregates the weighted data to obtain Dagg.Since compromised nodes send false sensing reports in order to deceive the base station, AlgorithmRDA considers trustworthiness of sensor nodes with respect to sensing function to increase thereliability of aggregated data. To achieve this, Aj weights data of each sensor node Ni with respect tothe sensor node’s trust value Ti,jsensing and Ti,jrouting . By weighting sensor data based on trustlevels, data aggregators reduce the compromised sensor nodes’ effect on the aggregated data. Thisreason is that a compromised node Ni is expected to have low Ti,jsensingand Ti,jroutingvalues asshown in next section.3.1 Experimental Simulation and ResultsThese various algorithms have their implemented results upon which simulations have carried out in ordertomeasure the performance parameters of the algorithms over the datasets. The results are summarized in the